1. Field
The present disclosure relates generally to processing different types of sensor data. Still more particularly, the present disclosure relates to a method and apparatus for registering different types of sensor data to a common model.
2. Background
Information about a scene may be identified using different types of sensor data. A scene may be any physical area for which sensor data can be generated. For example, without limitation, a scene may be an area in a city, a neighborhood, an area in a forest, an underwater region, a region of airspace, an area in a manufacturing facility, a room, a surface of a structure, or some other suitable type of scene.
The different types of sensor data that may be generated for a scene include, but are not limited to, acoustic data, biometric data, imaging data, voltage readings, vibration data, and other suitable types of sensor data. These different types of sensor data may be used in performing operations, such as, for example, without limitation, detecting the presence of objects in the scene, identifying the objects in the scene, tracking the movement of objects in the scene, detecting changes in an environment of the scene, measuring distances between objects in the scene, and other suitable operations.
As one illustrative example, different types of imaging data may be used for detecting, identifying, and/or tracking objects in a scene. The different types of imaging data may include, for example, without limitation, electro-optical (EO) images, infrared (IR) images, thermal images, radar images, ultraviolet images, and other suitable types of imaging data.
Oftentimes, sensor data generated from multiple sources may be combined such that the resulting information may be more accurate, more complete, and/or more reliable as compared to the sensor data generated by a single source. The process of combining the sensor data from the different sources may be referred to as “sensor fusion.” In particular, when the different sources are of the same modality, the process may be referred to as “uni-modal sensor fusion.” Further, when the different sources are of different modalities, the process may be referred to as “multi-modal sensor fusion.”
As one illustrative example of multi-modal sensor fusion, electro-optical images for a scene may be combined with infrared images for the same scene to generate overall information for the scene. This overall information may be used to track objects in the scene more accurately as compared to using only one of these types of images.
Oftentimes, performing sensor fusion for sensor data generated by different types of sources includes matching features between the different types of sensor data. For example, with currently-available systems for performing sensor fusion for two different types of images, features identified from the two different types of images may be matched. For example, features may be matched based on the features identified in the two different types of images having similar colors, brightness, shapes, and/or textures.
The identification of features in images is typically based on pixel values in the images. As a result, the accuracy of sensor fusion may depend on factors, such as, for example, sensor response, lighting, viewpoint of the sensor system, type of image, and/or other suitable factors. For example, matching features identified in two different types of images that are generated from different viewpoints may be more difficult than desired.
Therefore, it would be advantageous to have a method and apparatus that takes into account at least some of the issues discussed above, as well as possibly other issues.